In order to solve the above problems in multimodal irregular time-series events, in this paper, the following contributions is presented in this paper:
(1) We propose a new features fusion method to deal with multimodal data, where the features of complex data are fused into a common feature subspace. This method can be applied to different multimodal data.
(2) We explore different encoding methods for temporary features, and found a method to embed the temporary features into the non-temporary features, which allows us to better deal with time-series data
(3) We propose a model called FG-LSTM which developed from the Recurrent Neural Network such as Phased LSTM [1]} to deal with the problem of irregular time-series data. Our proposed model filters the input features by feature gate while recording the complex temporal relationship between different features.
(4) We compare with other models, and the experiment results based on the real data demonstrate that the prediction performance of our model is significantly improved.
